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Social bookmarking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures aka folksonomies. In this respect, needless to
do not have enough links between resources, and the LOD need a lot of time for creation. Therefore, this paper presents the new LOD conversion system that can convert the Web contents to the LOD. This system extracts keywords from sentences in the Web contents using DBpedia LOD, and generates the knowledge base. By
Twitter has become key for bringing awareness about real-world events, but the identification of event related posts goes beyond filtering keywords. Semantic enrichment using knowledge sources such as the Linked Open Data (LOD) cloud, has been proposed to deal with the poor textual contents of tweets for event
semantic enrichment of the media has allowed implement an improved search service, which attempts to overcome the limitations of traditional engines based on matching keywords.
In this paper we present a framework that extracts meaningful knowledge from microposts shared in social platforms in order to build user profiles. This process involves different steps for the analysis of such microposts (extraction of keywords, named entities and their matching to ontological concepts) and their
) information content due to occurrence of a property with respect to all the properties in a description base ii) unpredictability of an association due to participation of its properties in multiple domains iii) the extent of match between user specified keywords and properties and iv) the popularity of nodes involved in a
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